Efficiently Scale LLM Training Across a Large GPU Cluster with Alpa and Ray

This post presents how two open-source frameworks, Alpa.ai and Ray.io, work together to achieve the scale required to train a 175 billion-parameter JAX transformer model with pipeline parallelism. They enable the training of LLMs on large GPU clusters, automatically parallelizing computations and optimizing performance. The frameworks use techniques like interoperator and intraoperator parallelism and provide abstractions for GPU device management and communication.